{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T02:34:54Z","timestamp":1786242894537,"version":"3.56.0"},"reference-count":34,"publisher":"Emerald","issue":"7","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2021,7,5]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>In the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>A novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best\u2013worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Throughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>A novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-04-2020-0199","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T15:03:17Z","timestamp":1619622197000},"page":"1684-1703","source":"Crossref","is-referenced-by-count":36,"title":["Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology"],"prefix":"10.1108","volume":"121","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7744-9012","authenticated-orcid":false,"given":"Henry","family":"Lau","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6128-345X","authenticated-orcid":false,"given":"Yung Po","family":"Tsang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1535-5288","authenticated-orcid":false,"given":"Dilupa","family":"Nakandala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8577-4547","authenticated-orcid":false,"given":"Carman K.M.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"key":"key2022092814074732400_ref001","unstructured":"Bailey, T., Barriball, E., Dey, A. and Sankur, A. (2020), \u201cA practical approach to supply-chain risk management\u201d, available at: https:\/\/www.mckinsey.com\/business-functions\/operations\/our-insights\/a-practical-approach-to-supply-chain-risk-management (accessed 15 January 2021)."},{"key":"key2022092814074732400_ref002","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1016\/j.future.2019.07.059","article-title":"Predicting supply chain risks using machine learning: the trade-off between performance and interpretability","volume":"101","year":"2019","journal-title":"Future Generation Computer Systems"},{"key":"key2022092814074732400_ref003","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.promfg.2019.02.094","article-title":"Uncertain risk assessment modelling for bus body manufacturing supply chain using AHP and fuzzy AHP","volume":"30","year":"2019","journal-title":"Procedia Manufacturing"},{"issue":"5","key":"key2022092814074732400_ref004","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1108\/BIJ-09-2015-0090","article-title":"Select the best supply chain by risk analysis for Indian industries environment using MCDM approaches","volume":"24","year":"2017","journal-title":"Benchmarking: An International Journal"},{"key":"key2022092814074732400_ref005","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.ijpe.2016.08.021","article-title":"An orders-of-magnitude AHP supply chain risk assessment framework","volume":"182","year":"2016","journal-title":"International Journal of Production Economics"},{"issue":"7","key":"key2022092814074732400_ref006","doi-asserted-by":"publisher","first-page":"2322","DOI":"10.3390\/s18072322","article-title":"Levenberg-Marquardt neural network algorithm for degree of arteriovenous fistula stenosis classification using a dual optical photoplethysmography sensor","volume":"18","year":"2018","journal-title":"Sensors"},{"issue":"107972","key":"key2022092814074732400_ref007","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2020.107972","article-title":"Can supply chain risk management practices mitigate the disruption impacts on supply chains' resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era","volume":"233","year":"2021","journal-title":"International Journal of Production Economics"},{"issue":"3","key":"key2022092814074732400_ref008","first-page":"902","article-title":"Over-and under-estimation of risks and counteractive adjustment for cold chain operations","volume":"29","year":"2018","journal-title":"International Journal of Logistics Management"},{"issue":"10","key":"key2022092814074732400_ref009","first-page":"6532","article-title":"Efficient and privacy-enhanced federated learning for industrial artificial intelligence","volume":"16","year":"2019","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"9","key":"key2022092814074732400_ref010","doi-asserted-by":"publisher","first-page":"868","DOI":"10.3390\/app7090868","article-title":"A neural networks approach for improving the accuracy of multi-criteria recommender systems","volume":"7","year":"2017","journal-title":"Applied Sciences"},{"issue":"16","key":"key2022092814074732400_ref011","doi-asserted-by":"crossref","first-page":"5031","DOI":"10.1080\/00207543.2015.1030467","article-title":"Supply chain risk management: a literature review","volume":"53","year":"2015","journal-title":"International Journal of Production Research"},{"issue":"1","key":"key2022092814074732400_ref012","doi-asserted-by":"publisher","first-page":"154","DOI":"10.3390\/su12010154","article-title":"A neutrosophic AHP and TOPSIS framework for supply chain risk assessment in automotive industry of Pakistan","volume":"12","year":"2020","journal-title":"Sustainability"},{"issue":"4","key":"key2022092814074732400_ref013","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1007\/s10640-020-00438-9","article-title":"Environmental and regulatory concerns during the COVID-19 pandemic: results from the pandemic food and stigma survey","volume":"76","year":"2020","journal-title":"Environmental and Resource Economics"},{"key":"key2022092814074732400_ref014","doi-asserted-by":"publisher","DOI":"10.1108\/JIMA-10-2018-0206","article-title":"Prioritising the risks in Halal food supply chain: an MCDM approach","year":"2019","journal-title":"Journal of Islamic Marketing"},{"issue":"3","key":"key2022092814074732400_ref015","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/1097198X.2019.1642073","article-title":"Challenges faced by SMEs in globalizing their business: an interview with Hak-kyu Lim, Chief executive Officer of BG T&A, Korea and Philippine","volume":"22","year":"2019","journal-title":"Journal of Global Information Technology Management"},{"issue":"2","key":"key2022092814074732400_ref016","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.ejor.2017.08.019","article-title":"Pricing and sourcing strategies for competing retailers in supply chains under disruption risk","volume":"265","year":"2018","journal-title":"European Journal of Operational Research"},{"key":"key2022092814074732400_ref017","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854","article-title":"A review of applications in federated learning","volume":"149","year":"2020","journal-title":"Computers and Industrial Engineering"},{"key":"key2022092814074732400_ref018","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3046028","article-title":"When information freshness meets service latency in federated learning: a task-aware incentive scheme for smart industries","year":"2020","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"7","key":"key2022092814074732400_ref019","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1108\/IMDS-10-2017-0465","article-title":"Decision modeling of risks in pharmaceutical supply chains","volume":"118","year":"2018","journal-title":"Industrial Management and Data Systems"},{"issue":"14","key":"key2022092814074732400_ref040","doi-asserted-by":"crossref","first-page":"4180","DOI":"10.1080\/00207543.2016.1267413","article-title":"Development of a hybrid fresh food supply chain risk assessment model","volume":"55","year":"2017","journal-title":"International Journal of Production Research"},{"key":"key2022092814074732400_ref020","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.asoc.2014.06.032","article-title":"Comparison of neural network application for fuzzy and ANFIS approaches for multi-criteria decision making problems","volume":"24","year":"2014","journal-title":"Applied Soft Computing"},{"key":"key2022092814074732400_ref021","first-page":"428","article-title":"The logistics of the short food supply chain: a literature review","volume":"26","year":"2020","journal-title":"Sustainable Production and Consumption"},{"key":"key2022092814074732400_ref022","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/9653261","article-title":"A fuzzy-grey multicriteria decision making approach for green supplier selection in low-carbon supply chain","volume":"2017","year":"2017","journal-title":"Mathematical Problems in Engineering"},{"issue":"2014","key":"key2022092814074732400_ref023","first-page":"626","article-title":"Achieving supply chain resilience: the role of procurement","volume":"19\/5\/6","year":"2014","journal-title":"Supply Chain Management: An International Journal"},{"key":"key2022092814074732400_ref024","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.omega.2014.11.009","article-title":"Best-worst multi-criteria decision-making method","volume":"53","year":"2015","journal-title":"Omega"},{"issue":"3","key":"key2022092814074732400_ref025","first-page":"792","article-title":"Food cold chain management","volume":"29","year":"2018","journal-title":"International Journal of Logistics Management"},{"key":"key2022092814074732400_ref026","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/425740","article-title":"Review on methods to fix number of hidden neurons in neural networks","volume":"2013","year":"2013","journal-title":"Mathematical Problems in Engineering"},{"issue":"1","key":"key2022092814074732400_ref027","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1108\/IMDS-03-2015-0108","article-title":"Unlocking supply chain disruption risk within the Thai beverage industry","volume":"116","year":"2016","journal-title":"Industrial Management and Data Systems"},{"key":"key2022092814074732400_ref028","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.cie.2016.02.020","article-title":"Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques","volume":"102","year":"2016","journal-title":"Computers and Industrial Engineering"},{"issue":"3-4","key":"key2022092814074732400_ref029","first-page":"548","article-title":"Supply chain risk inter-relationships and mitigation in Indian scenario: an ISM-AHP integrated approach","volume":"32","year":"2019","journal-title":"International Journal of Logistics Systems and Management"},{"key":"key2022092814074732400_ref030","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.ijpe.2016.08.027","article-title":"Achieving competitive advantage through supply chain agility under uncertainty: a novel multi-criteria decision-making structure","volume":"190","year":"2017","journal-title":"International Journal of Production Economics"},{"issue":"2","key":"key2022092814074732400_ref031","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298981","article-title":"Federated machine learning: concept and applications","volume":"10","year":"2019","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"issue":"9","key":"key2022092814074732400_ref032","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1108\/IMDS-03-2016-0098","article-title":"A new risk assessment model for agricultural products cold chain logistics","volume":"117","year":"2017","journal-title":"Industrial management and data systems"},{"issue":"2","key":"key2022092814074732400_ref033","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1108\/K-10-2019-0653","article-title":"Operational risk modeling for cold chain logistics system: a Bayesian network approach","volume":"50","year":"2020","journal-title":"Kybernetes"}],"container-title":["Industrial Management &amp; Data Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IMDS-04-2020-0199\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IMDS-04-2020-0199\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:51:55Z","timestamp":1753393915000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/imds\/article\/121\/7\/1684-1703\/514188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,30]]},"references-count":34,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4,30]]},"published-print":{"date-parts":[[2021,7,5]]}},"alternative-id":["10.1108\/IMDS-04-2020-0199"],"URL":"https:\/\/doi.org\/10.1108\/imds-04-2020-0199","relation":{},"ISSN":["0263-5577"],"issn-type":[{"value":"0263-5577","type":"print"}],"subject":[],"published":{"date-parts":[[2021,4,30]]}}}